Exploration of Autistic Children using Case Based Reasoning System with Cognitive Map
Exploring an autistic child in Elementary school is a
difficult task that must be fully thought out and the teachers should
be aware of the many challenges they face raising their child
especially the behavioral problems of autistic children. Hence there
arises a need for developing Artificial intelligence (AI)
Contemporary Techniques to help diagnosis to discover autistic
people.
In this research, we suggest designing architecture of expert
system that combine Cognitive Maps (CM) with Case Based
Reasoning technique (CBR) in order to reduce time and costs of
traditional diagnosis process for the early detection to discover
autistic children. The teacher is supposed to enter child's information
for analyzing by CM module. Then, the reasoning processor would
translate the output into a case to be solved a current problem by
CBR module. We will implement a prototype for the model as a
proof of concept using java and MYSQL.
This will be provided a new hybrid approach that will achieve new
synergies and improve problem solving capabilities in AI. And we
will predict that will reduce time, costs, the number of human errors
and make expertise available to more people who want who want to
serve autistic children and their families.
[1] de Agentes Fsicos, R., Guidelines to apply CBR in real-time multi-agent
systems. JOURNAL OF PHYSICAL AGENTS, 2009.
[2] Cognitive Mapping. 2008 [cited; Available from:
http://intraspec.ca/cogmap.php.
[3] AITopics. Case Based Reasoning CBR 2010 [cited; Available from:
http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/CaseBased
Reasoning.
[4] Prentzas, J. and I. Hatzilygeroudis, Categorizing approaches combining
rule-based and case-based reasoning. Expert Systems-International
Journal of Knowledge Engineering, 2007. 24(2): p. 97-122.
[5] Plotnick, E., Concept mapping: A graphical system for understanding
the relationship between concepts: An ERIC digests. ERIC Document
Reproduction Service No. ED407938, 1997.
[6] Cormen, T.H., Introduction to algorithms. 2001: The MIT press.
[7] Mohamed, A.O., et al. Attention analysis in interactive software for
children with autism. 2006: ACM.
[8] Thompson, L. and M. Thompson. Quantitative Electroencephalogram
(QEEG) Findings & Neurofeedback Training. 2009 [cited; Available
from: http://www.addcentre.com/Pages/Autism/Texas07autism.htm.
[9] Melaka, M. ESSE: Learning Disability Classification System for Autism
and Dyslexia. 2009: Springer-Verlag New York Inc.
[10] Marling, C., E. Rissland, and A. Aamodt, Integrations with case-based
reasoning. The Knowledge Engineering Review, 2006. 20(03): p. 241-
245.
[11] Sehaba, K., V. Courboulay, and P. Estraillier, Interactive
system by observation and analysis of behavior for
children with autism. Technology and Disability, 2007: p.
181-188.
[1] de Agentes Fsicos, R., Guidelines to apply CBR in real-time multi-agent
systems. JOURNAL OF PHYSICAL AGENTS, 2009.
[2] Cognitive Mapping. 2008 [cited; Available from:
http://intraspec.ca/cogmap.php.
[3] AITopics. Case Based Reasoning CBR 2010 [cited; Available from:
http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/CaseBased
Reasoning.
[4] Prentzas, J. and I. Hatzilygeroudis, Categorizing approaches combining
rule-based and case-based reasoning. Expert Systems-International
Journal of Knowledge Engineering, 2007. 24(2): p. 97-122.
[5] Plotnick, E., Concept mapping: A graphical system for understanding
the relationship between concepts: An ERIC digests. ERIC Document
Reproduction Service No. ED407938, 1997.
[6] Cormen, T.H., Introduction to algorithms. 2001: The MIT press.
[7] Mohamed, A.O., et al. Attention analysis in interactive software for
children with autism. 2006: ACM.
[8] Thompson, L. and M. Thompson. Quantitative Electroencephalogram
(QEEG) Findings & Neurofeedback Training. 2009 [cited; Available
from: http://www.addcentre.com/Pages/Autism/Texas07autism.htm.
[9] Melaka, M. ESSE: Learning Disability Classification System for Autism
and Dyslexia. 2009: Springer-Verlag New York Inc.
[10] Marling, C., E. Rissland, and A. Aamodt, Integrations with case-based
reasoning. The Knowledge Engineering Review, 2006. 20(03): p. 241-
245.
[11] Sehaba, K., V. Courboulay, and P. Estraillier, Interactive
system by observation and analysis of behavior for
children with autism. Technology and Disability, 2007: p.
181-188.
@article{"International Journal of Business, Human and Social Sciences:58926", author = "Ebtehal Alawi Alsaggaf and Shehab A. Gamalel-Din", title = "Exploration of Autistic Children using Case Based Reasoning System with Cognitive Map", abstract = "Exploring an autistic child in Elementary school is a
difficult task that must be fully thought out and the teachers should
be aware of the many challenges they face raising their child
especially the behavioral problems of autistic children. Hence there
arises a need for developing Artificial intelligence (AI)
Contemporary Techniques to help diagnosis to discover autistic
people.
In this research, we suggest designing architecture of expert
system that combine Cognitive Maps (CM) with Case Based
Reasoning technique (CBR) in order to reduce time and costs of
traditional diagnosis process for the early detection to discover
autistic children. The teacher is supposed to enter child's information
for analyzing by CM module. Then, the reasoning processor would
translate the output into a case to be solved a current problem by
CBR module. We will implement a prototype for the model as a
proof of concept using java and MYSQL.
This will be provided a new hybrid approach that will achieve new
synergies and improve problem solving capabilities in AI. And we
will predict that will reduce time, costs, the number of human errors
and make expertise available to more people who want who want to
serve autistic children and their families.", keywords = "Autism, Cognitive Maps (CM), Case Based
Reasoning technique (CBR).", volume = "5", number = "1", pages = "73-5", }